Fish Audio vs Kyutai Moshi

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Fish Audio Audio	Kyutai Moshi Audio
Tagline	Expressive, emotion-controllable text-to-speech and voice cloning with an open-model heritage	Open-source, full-duplex speech-to-speech foundation model with sub-200ms latency
Category	Audio	Audio
Pricing	Freemium· Basic: $10 · Pro: $20 · Enterprise: Contact sales	Free· Free and open source. Models under CC-BY 4.0, code under MIT (Python) / Apache 2.0 (Rust). Self-hosted only — you pay your own compute (24GB+ GPU for PyTorch, or Apple Silicon via MLX).
Model	Fish Audio S2.1 Pro (in-house); S1 and S2 checkpoints open-sourced	Moshi (7B-class speech-text foundation model) + Mimi neural audio codec, in-house by Kyutai
Editorial score	—	—
Use cases	YouTube video voiceoverAudiobook narrationGame and animation character voicesCustomer support voice botsIVR and phone agentsAccessibility text-to-speechPodcast intro and ad readsReal-time streaming voice agentsInstant voice cloning for personal avatarsMultilingual dubbing	Real-time voice assistant prototypesResearch on full-duplex spoken dialogueOn-device voice interaction on Apple Silicon via MLXLow-latency conversational agents behind WebSocketNeural audio codec experimentation with MimiSelf-hosted voice interface for privacy-sensitive appsSpeech tokenizer for downstream audio LLM trainingInterruptible in-car or wearable voice UX
Pros	Emotion-tag control system ([angry], [whispering], [laughing], [pause]) gives fine prosodic steering that most TTS APIs lack Instant voice cloning from ~10-15 seconds of reference audio Voice Library of 2M+ community voices to browse instead of training your own 30+ language coverage across the same models Streaming API with low enough latency for real-time voice agents S1 and S2 model checkpoints published on GitHub for self-hosting Symmetric STT that recognises the same emotion tags used for TTS	Truly full-duplex — handles interruptions, overlap and back-channels rather than rigid turn-taking Sub-200ms practical latency on a single L4 GPU, well below third-party voice APIs Fully open weights (CC-BY 4.0) plus MIT/Apache code — self-host with no per-minute billing Ships with Mimi, a streaming neural audio codec that beats SpeechTokenizer and SemantiCodec Multiple inference backends: PyTorch for research, Rust/Candle for production, MLX for on-device Mac/iPhone Inner-monologue text prediction gives you a transcript alongside the audio stream for free
Cons	Free tier is explicitly non-commercial - monetised use requires a paid plan Public pricing is opaque - tier prices sit behind sign-in and shift with promos Community-uploaded voices raise consent and IP questions the platform pushes onto the user S2.1 Pro (the best model) is closed - only older S1/S2 are open source Cloning quality on non-English voices is more uneven than on English No native long-form audiobook chaptering workflow - you script and stitch yourself	English-only voices at launch — no multilingual support out of the box Knowledge and reasoning quality trail top text LLMs; it's a 7B-class model, not GPT-4o Voice Requires a 24GB+ GPU for the reference PyTorch build; on-device is only viable via MLX on Apple Silicon No hosted API or SaaS tier — you own the ops, scaling and safety filtering Only two fixed synthetic voices (Moshiko/Moshika); no voice cloning or speaker conditioning in the release
Website	fish.audio	kyutai.org

Pick Fish Audio if

✅ Emotion-tag control system ([angry], [whispering], [laughing], [pause]) gives fine prosodic steering that most TTS APIs lack
✅ Instant voice cloning from ~10-15 seconds of reference audio
✅ Voice Library of 2M+ community voices to browse instead of training your own
✅ 30+ language coverage across the same models

Pick Kyutai Moshi if

✅ Truly full-duplex — handles interruptions, overlap and back-channels rather than rigid turn-taking
✅ Sub-200ms practical latency on a single L4 GPU, well below third-party voice APIs
✅ Fully open weights (CC-BY 4.0) plus MIT/Apache code — self-host with no per-minute billing
✅ Ships with Mimi, a streaming neural audio codec that beats SpeechTokenizer and SemantiCodec

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